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E-grāmata: Meta-Analytic Structural Equation Modelling

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Meta-Analytic Structural Equation ModellingPalielināt
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This book explains how to employ MASEM, the combination of meta-analysis (MA) and structural equation modelling (SEM). It shows how by using MASEM, a single model can be tested to explain the relationships between a set of variables in several studies. This book gives an introduction to MASEM, with a focus on the state of the art approach: the two stage approach of Cheung and Cheung & Chan. Both, the fixed and the random approach to MASEM are illustrated with two applications to real data. All steps that have to be taken to perform the analyses are discussed extensively. All data and syntax files are available online, so that readers can imitate all analyses.By using SEM for meta-analysis, this book shows how to benefit from all available information from all available studies, even if few or none of the studies report about all relationships that feature in the full model of interest.

Introduction.- Methods of MASEM.- Heterogeneity in MASEM.- Issues in MASEM.- Example: Fitting a path model with the two-stage approach.- Example: Fitting a factor model with the two-stage approach.- Conclusions.
1 Introduction to Meta-Analysis and Structural Equation Modeling
1(14)
1.1 What Is Meta-Analysis?
1(3)
1.1.1 Issues in Meta-Analysis
2(1)
1.1.2 Statistical Analysis
3(1)
1.2 What Is SEM?
4(8)
1.2.1 Path Analysis
4(3)
1.2.2 Model Fit
7(2)
1.2.3 Factor Analysis
9(3)
1.3 Why Should You Combine SEM and MA?
12(3)
References
13(2)
2 Methods for Meta-Analytic Structural Equation Modeling
15(10)
2.1 Introduction
15(1)
2.2 Univariate Methods
16(1)
2.3 Multivariate Methods
17(8)
2.3.1 The GLS Method
17(2)
2.3.2 Two Stage Structural Equation Modeling (TSSEM)
19(3)
References
22(3)
3 Heterogeneity
25(8)
3.1 Introduction
25(1)
3.2 Testing the Significance of Heterogeneity
26(1)
3.3 The Size of the Heterogeneity
27(1)
3.4 Random Effects Analysis or Explaining Heterogeneity
28(5)
3.4.1 Random Effects MASEM
28(2)
3.4.2 Subgroup Analysis
30(1)
References
31(2)
4 Issues in Meta-Analytic Structural Equation Modeling
33(6)
4.1 Software to Conduct MASEM
33(2)
4.2 Fit-Indices in TSSEM
35(1)
4.3 Missing Correlations in TSSEM
35(1)
4.4 The ML-Approach to MASEM
36(3)
References
37(2)
5 Fitting a Path Model with the Two-Stage Approach
39(18)
5.1 Introduction
39(1)
5.2 Preparing the Data
40(2)
5.3 Fixed Effects Analysis
42(3)
5.4 Random Effects Analysis
45(8)
5.5 Random Effects Subgroup Analysis
53(4)
References
56(1)
6 Fitting a Factor Model with the Two-Stage Approach
57(14)
6.1 Introduction
57(1)
6.2 Preparing the Data
58(1)
6.3 Fixed Effects Analysis
59(2)
6.4 Random Effects Analysis
61(10)
References
69(2)
Appendix A Model Implied Covariance Matrix of the Example Path Model 71(2)
Appendix B Fitting a Path Model to a Covariance Matrix with OpenMx 73(8)
Appendix C Model Implied Covariance Matrix of the Example Factor Model 81(2)
Appendix D Fitting a Factor Model to a Covariance Matrix with OpenMx 83
Dr. Suzanne Jak is a researcher in the Faculty of Social and Behavioural Sciences at the University of Utrecht, Netherlands. She also works as a lecturer in Methods and Statistics at the department of Education of the University of Amsterdam.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97833192717432e.html
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